Method and apparatus for artificial intelligence acceleration

ABSTRACT

An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.

BACKGROUND

Deep learning artificial intelligence (AI) models are trained usinglarge amounts of data, e.g., terabytes or petabytes, which typicallymust be stored, moved and managed for model generation, modelverification, and later access as examples. All of the data movement andaccess takes time and consumes resources, local and remote, andcommunication bandwidth. Vector access and searching, and example accesscan create processing, network and data accessing bottlenecks. Thesefactors and more slow down AI processes and do not scale well withstandard processing and data storage as data amounts, AI model sizes anddepths increase, which are present trends. Therefore, there is a need inthe art for a solution which overcomes the drawbacks described above.

SUMMARY

In some embodiments, an apparatus for artificial intelligenceacceleration is provided. The apparatus includes a storage and computesystem having a distributed, redundant key value store for metadata. Thestorage and compute system having distributed compute resourcesconfigurable to access, through a plurality of authorities, data in thesolid-state memory, run inference with a deep learning model, generatevectors for the data and store the vectors in the key value store.

Other aspects and advantages of the embodiments will become apparentfrom the following detailed description taken in conjunction with theaccompanying drawings which illustrate, by way of example, theprinciples of the described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The described embodiments and the advantages thereof may best beunderstood by reference to the following description taken inconjunction with the accompanying drawings. These drawings in no waylimit any changes in form and detail that may be made to the describedembodiments by one skilled in the art without departing from the spiritand scope of the described embodiments.

The present disclosure is illustrated by way of example, and not by wayof limitation, and can be more fully understood with reference to thefollowing detailed description when considered in connection with thefigures as described below.

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of FIGS. 1-3 in accordance with someembodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 4 depicts a storage cluster with distributed metadata indexes andauthorities, as useful for artificial intelligence models and processesinvolving large amounts of unstructured data stored in the storagecluster.

FIG. 5 depicts further distributed resources in the storage cluster.

FIG. 6 depicts an artificial intelligence search through vectorspointing to examples in unstructured data, which can be performedefficiently by embodiments of the storage cluster.

FIG. 7 depicts a search through vectors to form bucket views linked toexamples in unstructured data contained in one or more buckets, whichcan be performed efficiently by embodiments of the storage cluster.

FIG. 8 depicts a search of tags in a tag index.

FIG. 9 is a flow diagram of a method of artificial intelligenceacceleration, which can be performed by embodiments of the storagecluster described herein as a storage and compute system.

FIG. 10 is a flow diagram of a method for an artificial intelligenceprocess, which can be performed by embodiments of the storage clusterdescribed herein.

FIG. 11 is an illustration showing an exemplary computing device whichmay implement the embodiments described herein.

DETAILED DESCRIPTION

Various embodiments of a storage system that can be optimized forartificial intelligence (AI) processes, including deep learning,inference, locality-preserving vector searching and bucket views, aredescribed herein. These storage systems have distributed computingresources and metadata indexes, scalable storage capacity andarchitecture, and redundancy for error correction and data and metadatarecovery, in various combinations. A storage cluster can be set up withsufficient storage capacity in solid-state storage to load in once, andthen hold unstructured data throughout the AI pipeline includinggeneration of vectors, searching, generation of bucket views, and accessof examples in storage memory, without having to move data back andforth or in and out of the storage and computing system during theseprocesses. AI models, tensors or vectors can be generated on-site orimported and attached to objects loaded into or already present instorage memory. It is possible to load in a large amount of data to thesystem, make multiple training sets, train one or more AI models, testout (verify) the model(s), and continue to retain the data in memory.

Continuous learning, incremental updates, model variations, modelcomparisons, etc., are supported with the data in residence. Alsosupported is large-scale data tiering, one example of which is movinglarge amounts of data such as terabytes or petabytes into the storagesystem for a set of inference data, then doing a massively paralleldeletion of all of this data when swapping out to another data set.Another example of large-scale data tiering supported combines asimilarity search with tiering, keeping a large data set in remotestorage, for example archival, cloud or glacier storage, but with theindex in the storage system. Bucket views can be created with thestorage and computing system on-site, and data can be pulled in fromremote storage when needed, then kept in the storage and computingsystem for AI pipelines performing multiple reads. This speeds up accessto the portion of a large data set that is relevant for a given use.When bucket views are dropped, objects can be dropped from the storagesystem unless linked to another bucket view, thus freeing up space forother bucket views. Various features from the following descriptions canbe combined in various embodiments, for optimization of various aspectsof artificial intelligence operations.

FIG. 1A illustrates an example system for data storage, in accordancewith some implementations. System 100 (also referred to as “storagesystem” herein) includes numerous elements for purposes of illustrationrather than limitation. It may be noted that system 100 may include thesame, more, or fewer elements configured in the same or different mannerin other implementations.

System 100 includes a number of computing devices 164. Computing devices(also referred to as “client devices” herein) may be for example, aserver in a data center, a workstation, a personal computer, a notebook,or the like. Computing devices 164 are coupled for data communicationsto one or more storage arrays 102 through a storage area network (SAN)158 or a local area network (LAN) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (SAS), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (ATA), Fibre Channel Protocol, Small Computer SystemInterface (SCSI), Internet Small Computer System Interface (iSCSI),HyperSCSI, Non-Volatile Memory Express (NVMe) over Fabrics, or the like.It may be noted that SAN 158 is provided for illustration, rather thanlimitation. Other data communication couplings may be implementedbetween computing devices 164 and storage arrays 102.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (TCP), UserDatagram Protocol (UDP), Internet Protocol (IP), HyperText TransferProtocol (HTTP), Wireless Access Protocol (WAP), Handheld DeviceTransport Protocol (HDTP), Session Initiation Protocol (SIP), Real TimeProtocol (RTP), or the like.

Storage arrays 102 may provide persistent data storage for the computingdevices 164. Storage array 102A may be contained in a chassis (notshown), and storage array 102B may be contained in another chassis (notshown), in implementations. Storage array 102A and 102B may include oneor more storage array controllers 110 (also referred to as “controller”herein). A storage array controller 110 may be embodied as a module ofautomated computing machinery comprising computer hardware, computersoftware, or a combination of computer hardware and software. In someimplementations, the storage array controllers 110 may be configured tocarry out various storage tasks. Storage tasks may include writing datareceived from the computing devices 164 to storage array 102, erasingdata from storage array 102, retrieving data from storage array 102 andproviding data to computing devices 164, monitoring and reporting ofdisk utilization and performance, performing redundancy operations, suchas Redundant Array of Independent Drives (RAID) or RAID-like dataredundancy operations, compressing data, encrypting data, and so forth.

Storage array controller 110 may be implemented in a variety of ways,including as a Field Programmable Gate Array (FPGA), a ProgrammableLogic Chip (PLC), an Application Specific Integrated Circuit (ASIC),System-on-Chip (SOC), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110 mayinclude, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110 may be independentlycoupled to the LAN 160. In implementations, storage array controller 110may include an I/O controller or the like that couples the storage arraycontroller 110 for data communications, through a midplane (not shown),to a persistent storage resource 170 (also referred to as a “storageresource” herein). The persistent storage resource 170 main include anynumber of storage drives 171 (also referred to as “storage devices”herein) and any number of non-volatile Random Access Memory (NVRAM)devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170 may be configured to receive, from the storage arraycontroller 110, data to be stored in the storage drives 171. In someexamples, the data may originate from computing devices 164. In someexamples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171. Inimplementations, the storage array controller 110 may be configured toutilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110 writes data directly tothe storage drives 171. In some implementations, the NVRAM devices maybe implemented with computer memory in the form of high bandwidth, lowlatency RAM. The NVRAM device is referred to as “non-volatile” becausethe NVRAM device may receive or include a unique power source thatmaintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171.

In implementations, storage drive 171 may refer to any device configuredto record data persistently, where “persistently” or “persistent” refersas to a device's ability to maintain recorded data after loss of power.In some implementations, storage drive 171 may correspond to non-diskstorage media. For example, the storage drive 171 may be one or moresolid-state drives (SSDs), flash memory based storage, any type ofsolid-state non-volatile memory, or any other type of non-mechanicalstorage device. In other implementations, storage drive 171 may includemay include mechanical or spinning hard disk, such as hard-disk drives(HDD).

In some implementations, the storage array controllers 110 may beconfigured for offloading device management responsibilities fromstorage drive 171 in storage array 102. For example, storage arraycontrollers 110 may manage control information that may describe thestate of one or more memory blocks in the storage drives 171. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110, thenumber of program-erase (P/E) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171 may be stored in oneor more particular memory blocks of the storage drives 171 that areselected by the storage array controller 110. The selected memory blocksmay be tagged with an identifier indicating that the selected memoryblock contains control information. The identifier may be utilized bythe storage array controllers 110 in conjunction with storage drives 171to quickly identify the memory blocks that contain control information.For example, the storage controllers 110 may issue a command to locatememory blocks that contain control information. It may be noted thatcontrol information may be so large that parts of the controlinformation may be stored in multiple locations, that the controlinformation may be stored in multiple locations for purposes ofredundancy, for example, or that the control information may otherwisebe distributed across multiple memory blocks in the storage drive 171.

In implementations, storage array controllers 110 may offload devicemanagement responsibilities from storage drives 171 of storage array 102by retrieving, from the storage drives 171, control informationdescribing the state of one or more memory blocks in the storage drives171. Retrieving the control information from the storage drives 171 maybe carried out, for example, by the storage array controller 110querying the storage drives 171 for the location of control informationfor a particular storage drive 171. The storage drives 171 may beconfigured to execute instructions that enable the storage drive 171 toidentify the location of the control information. The instructions maybe executed by a controller (not shown) associated with or otherwiselocated on the storage drive 171 and may cause the storage drive 171 toscan a portion of each memory block to identify the memory blocks thatstore control information for the storage drives 171. The storage drives171 may respond by sending a response message to the storage arraycontroller 110 that includes the location of control information for thestorage drive 171. Responsive to receiving the response message, storagearray controllers 110 may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171.

In other implementations, the storage array controllers 110 may furtheroffload device management responsibilities from storage drives 171 byperforming, in response to receiving the control information, a storagedrive management operation. A storage drive management operation mayinclude, for example, an operation that is typically performed by thestorage drive 171 (e.g., the controller (not shown) associated with aparticular storage drive 171). A storage drive management operation mayinclude, for example, ensuring that data is not written to failed memoryblocks within the storage drive 171, ensuring that data is written tomemory blocks within the storage drive 171 in such a way that adequatewear leveling is achieved, and so forth.

In implementations, storage array 102 may implement two or more storagearray controllers 110. For example, storage array 102A may includestorage array controllers 110A and storage array controllers 110B. At agiven instance, a single storage array controller 110 (e.g., storagearray controller 110A) of a storage system 100 may be designated withprimary status (also referred to as “primary controller” herein), andother storage array controllers 110 (e.g., storage array controller110A) may be designated with secondary status (also referred to as“secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170 (e.g., writing data to persistent storage resource170). At least some of the rights of the primary controller maysupersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170 when the primary controller has the right. Thestatus of storage array controllers 110 may change. For example, storagearray controller 110A may be designated with secondary status, andstorage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102. For example, storage array controller 110A maybe the primary controller for storage array 102A and storage array 102B,and storage array controller 110B may be the secondary controller forstorage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171. It may be notedthat in some implementations storage processing modules may be used toincrease the number of storage drives controlled by the primary andsecondary controllers.

In implementations, storage array controllers 110 are communicativelycoupled, via a midplane (not shown), to one or more storage drives 171and to one or more NVRAM devices (not shown) that are included as partof a storage array 102. The storage array controllers 110 may be coupledto the midplane via one or more data communication links and themidplane may be coupled to the storage drives 171 and the NVRAM devicesvia one or more data communications links. The data communications linksdescribed herein are collectively illustrated by data communicationslinks 108 and may include a Peripheral Component Interconnect Express(PCIe) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may similar to the storage array controllers 110 described withrespect to FIG. 1A. In one example, storage array controller 101 may besimilar to storage array controller 110A or storage array controller110B. Storage array controller 101 includes numerous elements forpurposes of illustration rather than limitation. It may be noted thatstorage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (RAM) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (CISC) microprocessor, reducedinstruction set computing (RISC) microprocessor, very long instructionword (VLIW) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (DDR4) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in a direct-mapped flash storagesystem. In one embodiment, a direct-mapped flash storage system is onethat that addresses data blocks within flash drives directly and withoutan address translation performed by the storage controllers of the flashdrives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103 that are coupled to the processing device 104 viaa data communications link 105. In implementations, host bus adapters103 may be computer hardware that connects a host system (e.g., thestorage array controller) to other network and storage arrays. In someexamples, host bus adapters 103 may be a Fibre Channel adapter thatenables the storage array controller 101 to connect to a SAN, anEthernet adapter that enables the storage array controller 101 toconnect to a LAN, or the like. Host bus adapters 103 may be coupled tothe processing device 104 via a data communications link 105 such as,for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (PCI) flash storage device 118 with separately addressablefast write storage. System 117 may include a storage controller 119. Inone embodiment, storage controller 119 may be a CPU, ASIC, FPGA, or anyother circuitry that may implement control structures necessaryaccording to the present disclosure. In one embodiment, system 117includes flash memory devices (e.g., including flash memory devices 120a-n), operatively coupled to various channels of the storage devicecontroller 119. Flash memory devices 120 a-n, may be presented to thecontroller 119 as an addressable collection of Flash pages, eraseblocks, and/or control elements sufficient to allow the storage devicecontroller 119 to program and retrieve various aspects of the Flash. Inone embodiment, storage device controller 119 may perform operations onflash memory devices 120A-N including storing and retrieving datacontent of pages, arranging and erasing any blocks, tracking statisticsrelated to the use and reuse of Flash memory pages, erase blocks, andcells, tracking and predicting error codes and faults within the Flashmemory, controlling voltage levels associated with programming andretrieving contents of Flash cells, etc.

In one embodiment, system 117 may include random access memory (RAM) 121to store separately addressable fast-write data. In one embodiment, RAM121 may be one or more separate discrete devices. In another embodiment,RAM 121 may be integrated into storage device controller 119 or multiplestorage device controllers. The RAM 121 may be utilized for otherpurposes as well, such as temporary program memory for a processingdevice (E.g., a central processing unit (CPU)) in the storage devicecontroller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119 may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiBand, etc.). Data communications links 123 a, 123 bmay be based on non-volatile memory express (NVMe) or NCMe over fabrics(NVMf) specifications that allow external connection to the storagedevice controller 119 from other components in the storage system 117.It should be noted that data communications links may be interchangeablyreferred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119 may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119 may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices107 a-120 n stored energy device 122 may power storage device controller119 and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a small computer system interface(SCSI) block storage array, a file server, an object server, a databaseor data analytics service, etc. The storage controllers 125 a, 125 b mayprovide services through some number of network interfaces (e.g., 126a-d) to host computers 127 a-n outside of the storage system 124.Storage controllers 125 a, 125 b may provide integrated services or anapplication entirely within the storage system 124, forming a convergedstorage and compute system. The storage controllers 125 a, 125 b mayutilize the fast write memory within or across storage devices 119 a-dto journal in progress operations to ensure the operations are not loston a power failure, storage controller removal, storage controller orstorage system shutdown, or some fault of one or more software orhardware components within the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119 may include mechanisms for implementinghigh availability primitives for use by other parts of a storage systemexternal to the Dual PCI storage device 118. For example, reservation orexclusion primitives may be provided so that, in a storage system withtwo storage controllers providing a highly available storage service,one storage controller may prevent the other storage controller fromaccessing or continuing to access the storage device. This could beused, for example, in cases where one controller detects that the othercontroller is not functioning properly or where the interconnect betweenthe two storage controllers may itself not be functioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as Peripheral Component Interconnect (PCI) Express, InfiniBand, andothers, are equally suitable. The chassis provides a port for anexternal communication bus for enabling communication between multiplechassis, directly or through a switch, and with client systems. Theexternal communication may use a technology such as Ethernet,InfiniBand, Fibre Channel, etc. In some embodiments, the externalcommunication bus uses different communication bus technologies forinter-chassis and client communication. If a switch is deployed withinor between chassis, the switch may act as a translation between multipleprotocols or technologies. When multiple chassis are connected to definea storage cluster, the storage cluster may be accessed by a client usingeither proprietary interfaces or standard interfaces such as networkfile system (NFS), common internet file system (CIFS), small computersystem interface (SCSI) or hypertext transfer protocol (HTTP).Translation from the client protocol may occur at the switch, chassisexternal communication bus or within each storage node. In someembodiments, multiple chassis may be coupled or connected to each otherthrough an aggregator switch. A portion and/or all of the coupled orconnected chassis may be designated as a storage cluster. As discussedabove, each chassis can have multiple blades, each blade has a MAC(media access control) address, but the storage cluster is presented toan external network as having a single cluster IP (Internet Protocol)address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, dynamic random access memory (DRAM) and interfaces for theinternal communication bus and power distribution for each of the powerbuses. Inside the storage node, the interfaces and storage unit share acommunication bus, e.g., PCI Express, in some embodiments. Thenon-volatile solid state memory units may directly access the internalcommunication bus interface through a storage node communication bus, orrequest the storage node to access the bus interface. The non-volatilesolid state memory unit contains an embedded central processing unit(CPU), solid state storage controller, and a quantity of solid statemass storage, e.g., between 2-32 terabytes (TB) in some embodiments. Anembedded volatile storage medium, such as DRAM, and an energy reserveapparatus are included in the non-volatile solid state memory unit. Insome embodiments, the energy reserve apparatus is a capacitor,super-capacitor, or battery that enables transferring a subset of DRAMcontents to a stable storage medium in the case of power loss. In someembodiments, the non-volatile solid state memory unit is constructedwith a storage class memory, such as phase change or magnetoresistiverandom access memory (MRAM) that substitutes for DRAM and enables areduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in FIG. 1, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 171 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 171 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 171 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 171, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (LDPC) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(RUSH) family of hashes, including Controlled Replication Under ScalableHashing (CRUSH). In some embodiments, pseudo-random assignment isutilized only for assigning authorities to nodes because the set ofnodes can change. The set of authorities cannot change so any subjectivefunction may be applied in these embodiments. Some placement schemesautomatically place authorities on storage nodes, while other placementschemes rely on an explicit mapping of authorities to storage nodes. Insome embodiments, a pseudorandom scheme is utilized to map from eachauthority to a set of candidate authority owners. A pseudorandom datadistribution function related to CRUSH may assign authorities to storagenodes and create a list of where the authorities are assigned. Eachstorage node has a copy of the pseudorandom data distribution function,and can arrive at the same calculation for distributing, and laterfinding or locating an authority. Each of the pseudorandom schemesrequires the reachable set of storage nodes as input in some embodimentsin order to conclude the same target nodes. Once an entity has beenplaced in an authority, the entity may be stored on physical devices sothat no expected failure will lead to unexpected data loss. In someembodiments, rebalancing algorithms attempt to store the copies of allentities within an authority in the same layout and on the same set ofmachines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (NIC) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (NVRAM)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (PLD) 208, e.g., a fieldprogrammable gate array (FPGA). In this embodiment, each flash die 222has pages, organized as sixteen kB (kilobyte) pages 224, and a register226 through which data can be written to or read from the flash die 222.In further embodiments, other types of solid-state memory are used inplace of, or in addition to flash memory illustrated within flash die222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA (field programmable gate array), flash memory206, and NVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS.2B and 2C) on a PCIe (peripheral component interconnect express) boardin a chassis 138 (see FIG. 2A). The storage unit 152 may be implementedas a single board containing storage, and may be the largest tolerablefailure domain inside the chassis. In some embodiments, up to twostorage units 152 may fail and the device will continue with no dataloss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources.

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster 161, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 168 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster 161, the systemautomatically rebalances load by:

Partitioning the new blade's 252 storage for use by the system'sauthorities 168,

Migrating selected authorities 168 to the new blade 252,

Starting endpoints 272 on the new blade 252 and including them in theswitch fabric's 146 client connection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (NLM) is utilized as a facility thatworks in cooperation with the Network File System (NFS) to provide aSystem V style of advisory file and record locking over a network. TheServer Message Block (SMB) protocol, one version of which is also knownas Common Internet File System (CIFS), may be integrated with thestorage systems discussed herein. SMP operates as an application-layernetwork protocol typically used for providing shared access to files,printers, and serial ports and miscellaneous communications betweennodes on a network. SMB also provides an authenticated inter-processcommunication mechanism. AMAZON™ S3 (Simple Storage Service) is a webservice offered by Amazon Web Services, and the systems described hereinmay interface with Amazon S3 through web services interfaces (REST(representational state transfer), SOAP (simple object access protocol),and BitTorrent). A RESTful API (application programming interface)breaks down a transaction to create a series of small modules. Eachmodule addresses a particular underlying part of the transaction. Thecontrol or permissions provided with these embodiments, especially forobject data, may include utilization of an access control list (ACL).The ACL is a list of permissions attached to an object and the ACLspecifies which users or system processes are granted access to objects,as well as what operations are allowed on given objects. The systems mayutilize Internet Protocol version 6 (IPv6), as well as IPv4, for thecommunications protocol that provides an identification and locationsystem for computers on networks and routes traffic across the Internet.The routing of packets between networked systems may include Equal-costmulti-path routing (ECMP), which is a routing strategy where next-hoppacket forwarding to a single destination can occur over multiple “bestpaths” which tie for top place in routing metric calculations.Multi-path routing can be used in conjunction with most routingprotocols, because it is a per-hop decision limited to a single router.The software may support Multi-tenancy, which is an architecture inwhich a single instance of a software application serves multiplecustomers. Each customer may be referred to as a tenant. Tenants may begiven the ability to customize some parts of the application, but maynot customize the application's code, in some embodiments. Theembodiments may maintain audit logs. An audit log is a document thatrecords an event in a computing system. In addition to documenting whatresources were accessed, audit log entries typically include destinationand source addresses, a timestamp, and user login information forcompliance with various regulations. The embodiments may support variouskey management policies, such as encryption key rotation. In addition,the system may support dynamic root passwords or some variationdynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or local area network (‘LAN’), or as some othermechanism capable of transporting digital information between thestorage system 306 and the cloud services provider 302. Such a datacommunications link 304 may be fully wired, fully wireless, or someaggregation of wired and wireless data communications pathways. In suchan example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using one or more data communications protocols.For example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using the handheld device transfer protocol(‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol(‘IP’), real-time transfer protocol (‘RTP’), transmission controlprotocol (‘TCP’), user datagram protocol (‘UDP’), wireless applicationprotocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides services tousers of the cloud services provider 302 through the sharing ofcomputing resources via the data communications link 304. The cloudservices provider 302 may provide on-demand access to a shared pool ofconfigurable computing resources such as computer networks, servers,storage, applications and services, and so on. The shared pool ofconfigurable resources may be rapidly provisioned and released to a userof the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services to the storage system 306 and users ofthe storage system 306 through the implementation of an infrastructureas a service (‘IaaS’) service model where the cloud services provider302 offers computing infrastructure such as virtual machines and otherresources as a service to subscribers. In addition, the cloud servicesprovider 302 may be configured to provide services to the storage system306 and users of the storage system 306 through the implementation of aplatform as a service (‘PaaS’) service model where the cloud servicesprovider 302 offers a development environment to application developers.Such a development environment may include, for example, an operatingsystem, programming-language execution environment, database, webserver, or other components that may be utilized by applicationdevelopers to develop and run software solutions on a cloud platform.Furthermore, the cloud services provider 302 may be configured toprovide services to the storage system 306 and users of the storagesystem 306 through the implementation of a software as a service(‘SaaS’) service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. The cloud services provider302 may be further configured to provide services to the storage system306 and users of the storage system 306 through the implementation of anauthentication as a service (‘AaaS’) service model where the cloudservices provider 302 offers authentication services that can be used tosecure access to applications, data sources, or other resources. Thecloud services provider 302 may also be configured to provide servicesto the storage system 306 and users of the storage system 306 throughthe implementation of a storage as a service model where the cloudservices provider 302 offers access to its storage infrastructure foruse by the storage system 306 and users of the storage system 306.Readers will appreciate that the cloud services provider 302 may beconfigured to provide additional services to the storage system 306 andusers of the storage system 306 through the implementation of additionalservice models, as the service models described above are included onlyfor explanatory purposes and in no way represent a limitation of theservices that may be offered by the cloud services provider 302 or alimitation as to the service models that may be implemented by the cloudservices provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. Public cloud and private clouddeployment models may differ and may come with various advantages anddisadvantages. For example, because a public cloud deployment involvesthe sharing of a computing infrastructure across different organization,such a deployment may not be ideal for organizations with securityconcerns, mission-critical workloads, uptime requirements demands, andso on. While a private cloud deployment can address some of theseissues, a private cloud deployment may require on-premises staff tomanage the private cloud. In still alternative embodiments, the cloudservices provider 302 may be embodied as a mix of a private and publiccloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat additional hardware components and additional software componentsmay be necessary to facilitate the delivery of cloud services to thestorage system 306 and users of the storage system 306. For example, thestorage system 306 may be coupled to (or even include) a cloud storagegateway. Such a cloud storage gateway may be embodied, for example, ashardware-based or software-based appliance that is located on premisewith the storage system 306. Such a cloud storage gateway may operate asa bridge between local applications that are executing on the storagesystem 306 and remote, cloud-based storage that is utilized by thestorage system 306. Through the use of a cloud storage gateway,organizations may move primary iSCSI or NAS to the cloud servicesprovider 302, thereby enabling the organization to save space on theiron-premises storage systems. Such a cloud storage gateway may beconfigured to emulate a disk array, a block-based device, a file server,or other storage system that can translate the SCSI commands, fileserver commands, or other appropriate command into REST-space protocolsthat facilitate communications with the cloud services provider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. Such applications may takemany forms in accordance with various embodiments of the presentdisclosure. For example, the cloud services provider 302 may beconfigured to provide access to data analytics applications to thestorage system 306 and users of the storage system 306. Such dataanalytics applications may be configured, for example, to receivetelemetry data phoned home by the storage system 306. Such telemetrydata may describe various operating characteristics of the storagesystem 306 and may be analyzed, for example, to determine the health ofthe storage system 306, to identify workloads that are executing on thestorage system 306, to predict when the storage system 306 will run outof various resources, to recommend configuration changes, hardware orsoftware upgrades, workflow migrations, or other actions that mayimprove the operation of the storage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include storage resources308, which may be embodied in many forms. For example, in someembodiments the storage resources 308 can include nano-RAM or anotherform of nonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate. In some embodiments, the storage resources 308may include 3D crosspoint non-volatile memory in which bit storage isbased on a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. In some embodiments, the storageresources 308 may include flash memory, including single-level cell(‘SLC’) NAND flash, multi-level cell (‘MLC’) NAND flash, triple-levelcell (‘TLC’) NAND flash, quad-level cell (‘QLC’) NAND flash, and others.In some embodiments, the storage resources 308 may include non-volatilemagnetoresistive random-access memory (‘MRAM’), including spin transfertorque (‘STT’) MRAM, in which data is stored through the use of magneticstorage elements. In some embodiments, the example storage resources 308may include non-volatile phase-change memory (‘PCM’) that may have theability to hold multiple bits in a single cell as cells can achieve anumber of distinct intermediary states. In some embodiments, the storageresources 308 may include quantum memory that allows for the storage andretrieval of photonic quantum information. In some embodiments, theexample storage resources 308 may include resistive random-access memory(‘ReRAM’) in which data is stored by changing the resistance across adielectric solid-state material. In some embodiments, the storageresources 308 may include storage class memory (‘SCM’) in whichsolid-state nonvolatile memory may be manufactured at a high densityusing some combination of sub-lithographic patterning techniques,multiple bits per cell, multiple layers of devices, and so on. Readerswill appreciate that other forms of computer memories and storagedevices may be utilized by the storage systems described above,including DRAM, SRAM, EEPROM, universal memory, and many others. Thestorage resources 308 depicted in FIG. 3A may be embodied in a varietyof form factors, including but not limited to, dual in-line memorymodules (‘DIMMs’), non-volatile dual in-line memory modules (‘NVDIMMs’),M.2, U.2, and others.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306. The communications resources 310may be configured to utilize a variety of different protocols and datacommunication fabrics to facilitate data communications betweencomponents within the storage systems as well as computing devices thatare outside of the storage system. For example, the communicationsresources 310 can include fibre channel (‘FC’) technologies such as FCfabrics and FC protocols that can transport SCSI commands over FCnetworks. The communications resources 310 can also include FC overethernet (‘FCoE’) technologies through which FC frames are encapsulatedand transmitted over Ethernet networks. The communications resources 310can also include InfiniB and (‘IB’) technologies in which a switchedfabric topology is utilized to facilitate transmissions between channeladapters. The communications resources 310 can also include NVM Express(‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologiesthrough which non-volatile storage media attached via a PCI express(‘PCIe’) bus may be accessed. The communications resources 310 can alsoinclude mechanisms for accessing storage resources 308 within thestorage system 306 utilizing serial attached SCSI (‘SAS’), serial ATA(‘SATA’) bus interfaces for connecting storage resources 308 within thestorage system 306 to host bus adapters within the storage system 306,internet small computer systems interface (‘iSCSI’) technologies toprovide block-level access to storage resources 308 within the storagesystem 306, and other communications resources that that may be usefulin facilitating data communications between components within thestorage system 306, as well as data communications between the storagesystem 306 and computing devices that are outside of the storage system306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or moreapplication-specific integrated circuits (‘ASICs’) that are customizedfor some particular purpose as well as one or more central processingunits (‘CPUs’). The processing resources 312 may also include one ormore digital signal processors (‘DSPs’), one or more field-programmablegate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or otherform of processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform various tasks. The software resources314 may include, for example, one or more modules of computer programinstructions that when executed by processing resources 312 within thestorage system 306 are useful in carrying out various data protectiontechniques to preserve the integrity of data that is stored within thestorage systems. Readers will appreciate that such data protectiontechniques may be carried out, for example, by system software executingon computer hardware within the storage system, by a cloud servicesprovider, or in other ways. Such data protection techniques can include,for example, data archiving techniques that cause data that is no longeractively used to be moved to a separate storage device or separatestorage system for long-term retention, data backup techniques throughwhich data stored in the storage system may be copied and stored in adistinct location to avoid data loss in the event of equipment failureor some other form of catastrophe with the storage system, datareplication techniques through which data stored in the storage systemis replicated to another storage system such that the data may beaccessible via multiple storage systems, data snapshotting techniquesthrough which the state of data within the storage system is captured atvarious points in time, data and database cloning techniques throughwhich duplicate copies of data and databases may be created, and otherdata protection techniques. Through the use of such data protectiontechniques, business continuity and disaster recovery objectives may bemet as a failure of the storage system may not result in the loss ofdata stored in the storage system.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

Readers will appreciate that the various components depicted in FIG. 3Bmay be grouped into one or more optimized computing packages asconverged infrastructures. Such converged infrastructures may includepools of computers, storage and networking resources that can be sharedby multiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may minimizecompatibility issues between various components within the storagesystem 306 while also reducing various costs associated with theestablishment and operation of the storage system 306. Such convergedinfrastructures may be implemented with a converged infrastructurereference architecture, with standalone appliances, with a softwaredriven hyper-converged approach (e.g., hyper-converged infrastructures),or in other ways.

Readers will appreciate that the storage system 306 depicted in FIG. 3Bmay be useful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. Such AI applications may enable devices to perceive theirenvironment and take actions that maximize their chance of success atsome goal. Examples of such AI applications can include IBM Watson,Microsoft Oxford, Google DeepMind, Baidu Minwa, and others. The storagesystems described above may also be well suited to support other typesof applications that are resource intensive such as, for example,machine learning applications. Machine learning applications may performvarious types of data analysis to automate analytical model building.Using algorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may be also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. A GPU is a modern processorwith thousands of cores, well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks have ignited a new wave of algorithmsand tools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing, andmany others. Training deep neural networks, however, requires both highquality input data and large amounts of computation. GPUs are massivelyparallel processors capable of operating on large amounts of datasimultaneously. When combined into a multi-GPU cluster, a highthroughput pipeline may be required to feed input data from storage tothe compute engines. Deep learning is more than just constructing andtraining models. There also exists an entire data pipeline that must bedesigned for the scale, iteration, and experimentation necessary for adata science team to succeed.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

A data scientist works to improve the usefulness of the trained modelthrough a wide variety of approaches: more data, better data, smartertraining, and deeper models. In many cases, there will be teams of datascientists sharing the same datasets and working in parallel to producenew and improved training models. Often, there is a team of datascientists working within these phases concurrently on the same shareddatasets. Multiple, concurrent workloads of data processing,experimentation, and full-scale training layer the demands of multipleaccess patterns on the storage tier. In other words, storage cannot justsatisfy large file reads, but must contend with a mix of large and smallfile reads and writes. Finally, with multiple data scientists exploringdatasets and models, it may be critical to store data in its nativeformat to provide flexibility for each user to transform, clean, and usethe data in a unique way. The storage systems described above mayprovide a natural shared storage home for the dataset, with dataprotection redundancy (e.g., by using RAID6) and the performancenecessary to be a common access point for multiple developers andmultiple experiments. Using the storage systems described above mayavoid the need to carefully copy subsets of the data for local work,saving both engineering and GPU-accelerated servers use time. Thesecopies become a constant and growing tax as the raw data set and desiredtransformations constantly update and change.

Readers will appreciate that a fundamental reason why deep learning hasseen a surge in success is the continued improvement of models withlarger data set sizes. In contrast, classical machine learningalgorithms, like logistic regression, stop improving in accuracy atsmaller data set sizes. As such, the separation of compute resources andstorage resources may also allow independent scaling of each tier,avoiding many of the complexities inherent in managing both together. Asthe data set size grows or new data sets are considered, a scale outstorage system must be able to expand easily. Similarly, if moreconcurrent training is required, additional GPUs or other computeresources can be added without concern for their internal storage.Furthermore, the storage systems described above may make building,operating, and growing an AI system easier due to the random readbandwidth provided by the storage systems, the ability to of the storagesystems to randomly read small files (50 KB) high rates (meaning that noextra effort is required to aggregate individual data points to makelarger, storage-friendly files), the ability of the storage systems toscale capacity and performance as either the dataset grows or thethroughput requirements grow, the ability of the storage systems tosupport files or objects, the ability of the storage systems to tuneperformance for large or small files (i.e., no need for the user toprovision filesystems), the ability of the storage systems to supportnon-disruptive upgrades of hardware and software even during productionmodel training, and for many other reasons.

Small file performance of the storage tier may be critical as many typesof inputs, including text, audio, or images will be natively stored assmall files. If the storage tier does not handle small files well, anextra step will be required to pre-process and group samples into largerfiles. Storage, built on top of spinning disks, that relies on SSD as acaching tier, may fall short of the performance needed. Because trainingwith random input batches results in more accurate models, the entiredata set must be accessible with full performance. SSD caches onlyprovide high performance for a small subset of the data and will beineffective at hiding the latency of spinning drives.

Readers will appreciate that the storage systems described above may beconfigured to support the storage of (among of types of data)blockchains. Such blockchains may be embodied as a continuously growinglist of records, called blocks, which are linked and secured usingcryptography. Each block in a blockchain may contain a hash pointer as alink to a previous block, a timestamp, transaction data, and so on.Blockchains may be designed to be resistant to modification of the dataand can serve as an open, distributed ledger that can recordtransactions between two parties efficiently and in a verifiable andpermanent way. This makes blockchains potentially suitable for therecording of events, medical records, and other records managementactivities, such as identity management, transaction processing, andothers.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniB and external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the systems described above may be bettersuited for the applications described above relative to other systemsthat may include, for example, a distributed direct-attached storage(DDAS) solution deployed in server nodes. Such DDAS solutions may bebuilt for handling large, less sequential accesses but may be less ableto handle small, random accesses. Readers will further appreciate thatthe storage systems described above may be utilized to provide aplatform for the applications described above that is preferable to theutilization of cloud-based resources as the storage systems may beincluded in an on-site or in-house infrastructure that is more secure,more locally and internally managed, more robust in feature sets andperformance, or otherwise preferable to the utilization of cloud-basedresources as part of a platform to support the applications describedabove. For example, services built on platforms such as IBM's Watson mayrequire a business enterprise to distribute individual user information,such as financial transaction information or identifiable patientrecords, to other institutions. As such, cloud-based offerings of AI asa service may be less desirable than internally managed and offered AIas a service that is supported by storage systems such as the storagesystems described above, for a wide array of technical reasons as wellas for various business reasons.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models.”

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. Likewise, machines likelocomotives and gas turbines that generate large amounts of informationthrough the use of a wide array of data-generating sensors may benefitfrom the rapid data processing capabilities of an edge solution. As anadditional example, some IoT devices such as connected video cameras maynot be well-suited for the utilization of cloud-based resources as itmay be impractical (not only from a privacy perspective, securityperspective, or a financial perspective) to send the data to the cloudsimply because of the pure volume of data that is involved. As such,many tasks that really on data processing, storage, or communicationsmay be better suited by platforms that include edge solutions such asthe storage systems described above.

Consider a specific example of inventory management in a warehouse,distribution center, or similar location. A large inventory,warehousing, shipping, order-fulfillment, manufacturing or otheroperation has a large amount of inventory on inventory shelves, and highresolution digital cameras that produce a firehose of large data. All ofthis data may be taken into an image processing system, which may reducethe amount of data to a firehose of small data. All of the small datamay be stored on-premises in storage. The on-premises storage, at theedge of the facility, may be coupled to the cloud, for external reports,real-time control and cloud storage. Inventory management may beperformed with the results of the image processing, so that inventorycan be tracked on the shelves and restocked, moved, shipped, modifiedwith new products, or discontinued/obsolescent products deleted, etc.The above scenario is a prime candidate for an embodiment of theconfigurable processing and storage systems described above. Acombination of compute-only blades and offload blades suited for theimage processing, perhaps with deep learning on offload-FPGA oroffload-custom blade(s) could take in the firehose of large data fromall of the digital cameras, and produce the firehose of small data. Allof the small data could then be stored by storage nodes, operating withstorage units in whichever combination of types of storage blades besthandles the data flow. This is an example of storage and functionacceleration and integration. Depending on external communication needswith the cloud, and external processing in the cloud, and depending onreliability of network connections and cloud resources, the system couldbe sized for storage and compute management with bursty workloads andvariable conductivity reliability. Also, depending on other inventorymanagement aspects, the system could be configured for scheduling andresource management in a hybrid edge/cloud environment.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. Big data analytics applications enable datascientists, predictive modelers, statisticians and other analyticsprofessionals to analyze growing volumes of structured transaction data,plus other forms of data that are often left untapped by conventionalbusiness intelligence (BI) and analytics programs. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form. Big data analytics is a form of advanced analytics,which involves complex applications with elements such as predictivemodels, statistical algorithms and what-if analyses powered byhigh-performance analytics systems.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Ski, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

FIG. 4 depicts a storage cluster 161 with distributed metadata indexes402 and authorities 168, as useful for artificial intelligence modelsand processes involving large amounts of unstructured data 408 stored inthe storage cluster 161. Authorities 168 access the unstructured data408 through a distributed key value store 404. Authorities 168, and insome embodiments other processes executing on blades 252 in the storagecluster 161, access vectors pointing to the unstructured data 408through a distributed key value store 406 for vectors. The storagecluster 161 could have one or more chassis 138 (see FIG. 2A), each withblades 252, implementing a computing and storage system. It should beappreciated that the embodiments are not limited to a distributed keyvalue store as a distributed hash table or a special tree structure maybe utilized.

As is the case for other authority-centric processes in the storagecluster 161, many (but not necessarily all) of the storage nodes 150each have multiple authorities 168 (or at least one authority 168) and aportion of whichever distributed resource is under consideration. Eachof the key value stores 404, 406 has portions distributed to the storagenodes 150, in blades 252, with each portion of a key value store 404,406 having metadata owned by and accessed by the authorities 168resident on that storage node 150 and blade 252, or in some embodimentsowned by and accessed by authorities 168 resident on various storagenodes 150 and blades 252 in the system. Key value stores 404, 406 (orother indexes 402) could be implemented in NVRAM 204 or flash memory206, or both, e.g., in storage units 152, with redundancy forreliability and recovery. In some embodiments, the key value store 406for vectors could be exposed as an interface for users, through an API(application programming interface).

FIG. 5 depicts further distributed resources in some embodiments of thestorage cluster 161. As in other embodiments, one or more of the storagenodes 150 in blades 252 could each be a compute only storage node 150.The distributed compute resources 502 are distributed across the storagenodes 150, in blades 252, e.g. as shown in the compute planes 256 inFIG. 2E, and can include processors, processor clusters, GPUs, customASICs, other compute agents, and/or FPGAs, etc., in various embodimentsas described above. A distributed inference engine 504 could be composedof some of the distributed compute resources 502, and is implemented insoftware, firmware, hardware or combination thereof, to performinference on the unstructured data 408, for example to generate vectorsfor the distributed key value store 406 for vectors. A distributeddelete 506 is implemented with similar resources, and is used byauthorities 168 for large (or small) amounts of file deletion such asoccur during a project tear down, e.g., when changing over from one verylarge data set to another. Each authority 168 deletes data owned by thatauthority 168, in parallel with other authorities 168, within the rangeof data being deleted (e.g. one or more files). In some embodiments adistributed search engine may be striped across the storage nodes 150,in blades 252.

RDMA (remote direct memory access) 508 is implemented in some versionsof the storage cluster 161, with each storage node 150 and/or eachstorage unit 152 having an RDMA unit so the storage nodes and/or storageunits 152 can communicate directly with each other with high bandwidthdata transfers for increased data throughput. This puts the distributedcompute resources 502 directly to work on the unstructured data 408, forexample through pipelines in some embodiments. In some versions, RDMA508 is available for external accesses. It should be appreciated thatthat the embodiments may utilize other communication protocols incombination or instead of RDMA as the embodiments are not limited toRDMA. In some embodiments high bandwidth communication protocols may beintegrated with the embodiments.

FIG. 6 depicts an artificial intelligence search 608 through vectorspointing to examples 616 in unstructured data 408, which can beperformed efficiently by embodiments of the storage cluster 161. Twoways by which the vectors could be deposited in the distributed keyvalue store 406 for vectors are for the storage system to generate thevectors, using the distributed inference engine 504, from unstructureddata 408 stored in the storage cluster 161, or to import the vectorsfrom another source, e.g., remote or cloud-based. To perform the search608 in the unstructured data 408 for examples related to an example 602,(e.g., to find data of an audio, image, video, text or other sample inthe examples 616), one of the distributed compute resources 502, or anauthority 168, performs a hash function 604 on a vector 618 of theexample 602 and generates a hash value 606. The hash value is searchedin a vector index 610, for example the distributed key value store 406for vectors, which should be locality-preserving for this embodiment.The search 608 finds one or more vectors that are within a specifieddistance of the vector for the example 602, using a locality-preservinghash operation that points to full vectors. With the results of thesearch 608, a full vector, pointing to (data of) one of the examples 616in the unstructured data 408, is found among the full vectors 612.Authorities 168 use the full vector to look up in a data index 614, suchas the distributed key value store 404 for authorities and data, andfind one or more pointers to the specific unstructured data 408.Authorities 168 can then access the data, to retrieve the pointed-toexample from the unstructured data 408. It should be appreciated thatthe term “examples” as with reference to FIG. 6 refers to the data thecustomer is interested in as well as the items the deep learningalgorithm is working on. In addition, while some embodiments mentionutilization of hash functions, this is not meant to be limiting as treelike structure, such as R-trees, or similar data structures may beintegrated with the embodiments.

As described above, the authorities 168 control data striping and eachown an authority-specific range of the data. Storage capacity of thestorage cluster 161 is expandable and computing and data accessingprocesses scale well with capacity as a result of the distributedprocesses and data structures. Thus, users can arrange the storagesystem to have sufficient storage capacity in solid-state storage memoryto hold all of the unstructured data on which the vectors are based, sothat the access based on the search has a 100% hit rate. Under suchcircumstances, it should not be necessary to move data back and forth toand from a remote location. Further, it is possible to hold all of theunstructured data 408 in the solid-state storage memory of the storagecluster 161 during generation of the vectors, as well as throughoutsearching the vectors and accessing the examples in the unstructureddata, with sufficient storage capacity arranged. In some embodiments,not all of the unstructured data is kept in flash or cannot be kept inflash. In some embodiment, the vector index is stored inside the storagesystem with pointers to an external store, cloud, tapes, etc., where theunstructured data in located. In some embodiments, the metadata (filenames, create dates, sizes, permissions) is stored in the storagesystem, which allows for the relatively quick creation of bucket viewsthat can be consumed immediately for metadata operations (e.g. listingthe object names, sizes and owners). These embodiments also enable theillustration the space required to page in all the data for a bucketview given the predicate. Thus, the user has the ability to select fromhaving a “fast bucket view” where objects are paged into flash, or a“slow bucket view” where the metadata is served by the storage system,but the data itself is streamed from the remote location through thestorage system. Paging in the data may be performed in the background oron demand. In some embodiments, status bars are shown in the graphicaluser interface (GUI) while the objects are paged in, in the background.

FIG. 7 depicts a search 714 through vectors 706 to form bucket views 708linked to examples 616 in unstructured data 408 contained in one or morebuckets 702, which can be performed efficiently by embodiments of thestorage cluster 161. Buckets 702 are a type of data structure thatcontain objects, which can be various types of data (including files,portions of data, data structures or unstructured data). The distributedcompute resources 502 store metadata for the buckets 702 as vectors 706,a form of metadata 704. In one embodiment, these vectors 706 are storedin the distributed key value store 406 for vectors. Upon receipt orgeneration of a similarity predicate 712, the distributed computeresources 502 perform a search 714 through the vectors 706, e.g., in thedistributed key value store 406, and generate bucket views 708 thatcontain links 710. The storage system can access the examples 616, basedon the links, through the authorities 168 and the distributed key valuestore 404 for authorities and data. It should be appreciated that bucketviews behave like a bucket but the instances do not live in the bucketas they are references to in the bucket view, which can be accessed likea bucket. Many generalizations can be extended from this mechanism wherethe bucket view is essentially a view table for objects. Onegeneralization applies to replication, where the replication epochs aretemporal views of the bucket, i.e., they are every PUT that happens overa range of time. In some embodiments, any PUT is echoed (make areference) into the view bucket. Another generalization may extend toseeing NFS file systems in the object world in some embodiments. Usingthis mechanism could make a procedural view that intercepted GETS andPUTS, heads of lists, etc., against the bucket and translated them intoNFS RPCs. This view can be attached as view logic or translation logicon the fake bucket (synthetic view). In some embodiments the mechanismmay be extended to HTFS and name nodes.

FIG. 8 depicts a search 806 of tags 802 in a tag index 804. The tagindex 804 could be implemented as a distributed key value store, forexample as part of the key value store 406 for vectors, or a separateone. Tags 802 are associated with the examples 616 in unstructured data408, and could be machine-generated locally or remotely, imported, oruser-generated. The search 806, using distributed compute resources 502,generates a list of examples 808, or account of examples 808, or otherdata regarding the examples 808 that have tags 802 satisfying apredicate (e.g., a similarity predicate 712 as used in the bucketsearch).

FIG. 9 is a flow diagram of a method of artificial intelligenceacceleration, which can be performed by embodiments of the storagecluster described herein as a storage and compute system. In an action902, a first key value store is established, for metadata of authoritiesrelating to data. The key value store is a distributed key value store,across the storage nodes of a storage cluster, in some embodiments. Inan action 904, a second key value store is established, for vectorscorresponding to examples in unstructured data. The key value store is adistributed key value store, across storage nodes of a storage cluster,in some embodiments. In an action 906, vectors are searched for in thesecond key value store. In an action 908, examples are accessed in theunstructured data, through authorities and the first key value store,based on the search of the vectors.

FIG. 10 is a flow diagram of a method for an artificial intelligenceprocess, which can be performed by embodiments of the storage clusterdescribed herein. In an action 1002, data is accessed in the storagesystem, through authorities in the storage system. In an action 1004,the storage system runs inference, with a deep learning model. In anaction 1006, the storage system generates vectors for data. In an action1008, the vectors are stored in a key value store. The key value storeis a distributed key value store, across storage nodes of a storagecluster, in some embodiments. In an action 1010, the storage systemreceives an object or a file. In an action 1012, the storage system runsa similarity search for object or file against vectors in key valuestore. In an action 1014, the storage system outputs a response and/orbucket view based on vector similarity search. In some embodiments, themethod may include training a new deep learning model using content ofthe bucket view as training data.

It should be appreciated that the methods described herein may beperformed with a digital processing system, such as a conventional,general-purpose computer system. Special purpose computers, which aredesigned or programmed to perform only one function may be used in thealternative. FIG. 11 is an illustration showing an exemplary computingdevice which may implement the embodiments described herein. Thecomputing device of FIG. 11 may be used to perform embodiments of thefunctionality for me AI processes in accordance with some embodiments.The computing device includes a central processing unit (CPU) 1101,which is coupled through a bus 1105 to a memory 1103, and mass storagedevice 1107. Mass storage device 1107 represents a persistent datastorage device such as a floppy disc drive or a fixed disc drive, whichmay be local or remote in some embodiments. Memory 1103 may include readonly memory, random access memory, etc. Applications resident on thecomputing device may be stored on or accessed via a computer readablemedium such as memory 1103 or mass storage device 1107 in someembodiments. Applications may also be in the form of modulatedelectronic signals modulated accessed via a network modem or othernetwork interface of the computing device. It should be appreciated thatCPU 1101 may be embodied in a general-purpose processor, a specialpurpose processor, or a specially programmed logic device in someembodiments.

Display 1111 is in communication with CPU 1101, memory 1103, and massstorage device 1107, through bus 1105. Display 1111 is configured todisplay any visualization tools or reports associated with the systemdescribed herein. Input/output device 1109 is coupled to bus 1105 inorder to communicate information in command selections to CPU 1101. Itshould be appreciated that data to and from external devices may becommunicated through the input/output device 1109. CPU 1101 can bedefined to execute the functionality described herein to enable thefunctionality described with reference to FIGS. 1-10. The code embodyingthis functionality may be stored within memory 1103 or mass storagedevice 1107 for execution by a processor such as CPU 1101 in someembodiments. The operating system on the computing device may be MSDOS™, MS-WINDOWS™, OS/2™, UNIX™, LINUX™, or other known operatingsystems. It should be appreciated that the embodiments described hereinmay also be integrated with a virtualized computing system that isimplemented with physical computing resources.

Detailed illustrative embodiments are disclosed herein. However,specific functional details disclosed herein are merely representativefor purposes of describing embodiments. Embodiments may, however, beembodied in many alternate forms and should not be construed as limitedto only the embodiments set forth herein.

It should be understood that although the terms first, second, etc. maybe used herein to describe various steps or calculations, these steps orcalculations should not be limited by these terms. These terms are onlyused to distinguish one step or calculation from another. For example, afirst calculation could be termed a second calculation, and, similarly,a second step could be termed a first step, without departing from thescope of this disclosure. As used herein, the term “and/or” and the “I”symbol includes any and all combinations of one or more of theassociated listed items.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes”, and/or “including”, when used herein, specifythe presence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. Therefore, the terminology usedherein is for the purpose of describing particular embodiments only andis not intended to be limiting.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

With the above embodiments in mind, it should be understood that theembodiments might employ various computer-implemented operationsinvolving data stored in computer systems. These operations are thoserequiring physical manipulation of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated. Further, the manipulationsperformed are often referred to in terms, such as producing,identifying, determining, or comparing. Any of the operations describedherein that form part of the embodiments are useful machine operations.The embodiments also relate to a device or an apparatus for performingthese operations. The apparatus can be specially constructed for therequired purpose, or the apparatus can be a general-purpose computerselectively activated or configured by a computer program stored in thecomputer. In particular, various general-purpose machines can be usedwith computer programs written in accordance with the teachings herein,or it may be more convenient to construct a more specialized apparatusto perform the required operations.

A module, an application, a layer, an agent or other method-operableentity could be implemented as hardware, firmware, or a processorexecuting software, or combinations thereof. It should be appreciatedthat, where a software-based embodiment is disclosed herein, thesoftware can be embodied in a physical machine such as a controller. Forexample, a controller could include a first module and a second module.A controller could be configured to perform various actions, e.g., of amethod, an application, a layer or an agent.

The embodiments can also be embodied as computer readable code on atangible non-transitory computer readable medium. The computer readablemedium is any data storage device that can store data, which can bethereafter read by a computer system. Examples of the computer readablemedium include hard drives, network attached storage (NAS), read-onlymemory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes,and other optical and non-optical data storage devices. The computerreadable medium can also be distributed over a network coupled computersystem so that the computer readable code is stored and executed in adistributed fashion. Embodiments described herein may be practiced withvarious computer system configurations including hand-held devices,tablets, microprocessor systems, microprocessor-based or programmableconsumer electronics, minicomputers, mainframe computers and the like.The embodiments can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a wire-based or wireless network.

Although the method operations were described in a specific order, itshould be understood that other operations may be performed in betweendescribed operations, described operations may be adjusted so that theyoccur at slightly different times or the described operations may bedistributed in a system which allows the occurrence of the processingoperations at various intervals associated with the processing.

In various embodiments, one or more portions of the methods andmechanisms described herein may form part of a cloud-computingenvironment. In such embodiments, resources may be provided over theInternet as services according to one or more various models. Suchmodels may include Infrastructure as a Service (IaaS), Platform as aService (PaaS), and Software as a Service (SaaS). In IaaS, computerinfrastructure is delivered as a service. In such a case, the computingequipment is generally owned and operated by the service provider. Inthe PaaS model, software tools and underlying equipment used bydevelopers to develop software solutions may be provided as a serviceand hosted by the service provider. SaaS typically includes a serviceprovider licensing software as a service on demand. The service providermay host the software, or may deploy the software to a customer for agiven period of time. Numerous combinations of the above models arepossible and are contemplated.

Various units, circuits, or other components may be described or claimedas “configured to” or “configurable to” perform a task or tasks. In suchcontexts, the phrase “configured to” or “configurable to” is used toconnote structure by indicating that the units/circuits/componentsinclude structure (e.g., circuitry) that performs the task or tasksduring operation. As such, the unit/circuit/component can be said to beconfigured to perform the task, or configurable to perform the task,even when the specified unit/circuit/component is not currentlyoperational (e.g., is not on). The units/circuits/components used withthe “configured to” or “configurable to” language include hardware—forexample, circuits, memory storing program instructions executable toimplement the operation, etc. Reciting that a unit/circuit/component is“configured to” perform one or more tasks, or is “configurable to”perform one or more tasks, is expressly intended not to invoke 35 U.S.C.112, sixth paragraph, for that unit/circuit/component. Additionally,“configured to” or “configurable to” can include generic structure(e.g., generic circuitry) that is manipulated by software and/orfirmware (e.g., an FPGA or a general-purpose processor executingsoftware) to operate in manner that is capable of performing the task(s)at issue. “Configured to” may also include adapting a manufacturingprocess (e.g., a semiconductor fabrication facility) to fabricatedevices (e.g., integrated circuits) that are adapted to implement orperform one or more tasks. “Configurable to” is expressly intended notto apply to blank media, an unprogrammed processor or unprogrammedgeneric computer, or an unprogrammed programmable logic device,programmable gate array, or other unprogrammed device, unlessaccompanied by programmed media that confers the ability to theunprogrammed device to be configured to perform the disclosedfunction(s).

The foregoing description, for the purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the embodiments and its practical applications, to therebyenable others skilled in the art to best utilize the embodiments andvarious modifications as may be suited to the particular usecontemplated. Accordingly, the present embodiments are to be consideredas illustrative and not restrictive, and the invention is not to belimited to the details given herein, but may be modified within thescope and equivalents of the appended claims.

What is claimed is:
 1. An apparatus for artificial intelligence acceleration, comprising: a storage and compute system having a distributed, redundant key value store for metadata; and the storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store, the distributed compute resources configurable to store metadata for one or more buckets containing the data and create bucket views containing links to objects within the data that are contained in the one or more buckets and stored in the solid-state memory.
 2. The apparatus of claim 1, further comprising: the storage and compute system having at least one network port, and configurable to receive an object or file, run the inference for the object or the file with the deep learning model, based on the vectors in the key value store, and output a response based on the inference.
 3. The apparatus of claim 1, wherein storage capacity of the solid-state memory is sufficient to hold all of the data for the inference with the deep learning model throughout the processing of an entire data pipeline.
 4. The apparatus of claim 1, wherein the plurality of authorities are configurable for distributed file deletion in accordance with the data identifiers owned by each authority.
 5. The apparatus of claim 1, wherein the links satisfy a similarity predicate, based on the vectors in the key value store.
 6. The apparatus of claim 1, further comprising: the distributed compute resources configurable to compute a vector of an object for a search, perform a hash operation on the vector, search for vectors in the key value store that are within a specified distance of the vector, based on a result of the hash operation, and retrieve one or more full vectors for objects stored in the plurality of solid-state memory storages based on a result of the search, wherein the vectors are stored in the key value store using a locality-preserving hash operation.
 7. The apparatus of claim 1, further comprising: the distributed compute resources configurable to receive tags associated with the data, and generate a list or count of data entities having tags that satisfy a predicate.
 8. A method for artificial intelligence acceleration, performed by a storage and compute system, the method comprising: accessing data in the system through a plurality of authorities; performing inference with a deep learning model; generating vectors for the data based on the performing; storing the vectors in a key value store of the system; storing metadata for one or more buckets containing examples in the unstructured data; and generating bucket views containing links to the examples.
 9. The method of claim 8, further comprising: receiving an object or file; and performing the inference for the object or file with the deep learning model based on vectors in the key value store.
 10. The method of claim 8, further comprising: outputting a response based on the inference.
 11. The method of claim 8, wherein the system is configurable to write and receive unstructured data into solid state memory of the system.
 12. The method of claim 8, wherein the key value store is a distributed key value store, across storage nodes of the system.
 13. The apparatus of claim 9, wherein the links to the examples satisfy a similarity predicate, based on the search of the vectors.
 14. A tangible, non-transitory, computer-readable media having instructions thereupon which, when executed by a processor, cause the processor to perform a method comprising: accessing data in the system through a plurality of authorities; performing inference with a deep learning model; generating vectors for the data based on the performing; storing the vectors in a key value store of the system; storing metadata for one or more buckets containing examples in the unstructured data; and generating bucket views containing links to the examples.
 15. The computer readable media of claim 14, wherein the method further comprises: receiving an object or file; and performing the inference for the object or file with the deep learning model based on vectors in the key value store.
 16. The computer readable media of claim 14, wherein the method further comprises: outputting a response based on the inference.
 17. The method of claim 14, wherein the system is configurable to write and receive unstructured data into solid state memory of the system.
 18. The method of claim 14, wherein the key value store is a distributed key value store, across storage nodes of the system.
 19. The computer readable media of claim 14, wherein the links to the examples satisfy a similarity predicate, based on the search of the vectors. 